• 한국프랜차이즈경영연구
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• 제5권1호
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• pp.49-64
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• 2014

본 연구는 대표 눈높이 러닝센터의 사업화 사례 분석을 통해 교육 서비스 프랜차이즈 기업의 자기주도 학습관 사업화에 대한 개념을 정립하는데 목적이 있다. 대교 눈높이 러닝센터는 자기주도 학습관과 관련된 프랜차이즈 사업화의 선두주자로 해당 산업 내에서 이러한 성공을 이끈 경영방식을 유지하고 있다.대교가 성공적으로 러닝센터 사업화를 이룰 수 있던 것은 목표관리, 학습관리, 그리고 환경관리 등의 3가지 요인을 기반으로 한 교육서비스를 제공하고 있기 때문이다. 첫째, 대교는 목표관리로 꿈과 학습목표 및 학습실천 계획을 세우고 실천할 수 있는 분위기를 조성함으로써 자기주도적 태도를 형성하는데 도움을 준다. 또한, 대교는 학습 성향검사를 통한 효율적인 학습방법을 탐색하고 도모하게 할 수 있는 정보를 제공한다. 그리고 대교는 지속적인 학습 동기부여를 위한 다양한 행사를 실시하고 있다. 둘째는 학습관리로서, 대교는 30여 년 노하우의 눈높이 교재를 통한 체계적인 기초학력을 정착하는데 도움을 주고, 학습자 중심의 개인별 맞춤 솔루션 제공 및 정확한 진도를 관리하며, 출결시스템을 통한 학습시간 관리 및 1:1 학습지도를 통한 학습실천 관리를 제공한다. 셋째는 환경관리로서, 대교는 이를 위해 과목별 담당교사 및 집중력 있는 시설을 통해 자기주도 학습을 위한 공부환경을 조성해주고, 멀티미디어 시스템을 통한 LAB학습, 동영상 학습을 통한 다양하고 재미있는 공부공간을 제공해준다.

• 한국전자거래학회지
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• 제25권2호
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• pp.15-27
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• 2020

본 연구는 일정계획을 위한 간트 차트(Gantt Chart) 생성과정을 세로로 세우면 일자형만 존재하는 테트리스(Tetris) 게임과 유사하다는 아이디어에서 출발하였다. 테트리스 게임에서 X축은 M개의 설비(Machine)들이 되고 Y축은 시간이 된다. 모든 설비에서 모든 종류(Type)의 주문은 분리 없이 작업 가능하나 작업물 종류가 다를 경우에는 시간지체 없이 작업 준비비용(SetupCost)이 발생한다는 가정이다. 본 연구에서는 앞에서 설명한 게임을 간트리스(Gantris)라 명명하고 게임환경을 구현 하였으며, 심층 강화학습을 통해서 학습한 인공지능이 실시간 스케줄링한 일정계획과 인간이 실시간으로 게임을 통해 수립한 일정계획을 비교하였다. 비교연구에서 학습환경은 단일 주문목록 학습환경과 임의 주문목록 학습환경에서 학습하였다. 본 연구에서 수행한 비교대상 시스템은 두 가지로 4개의 머신(Machine)-2개의 주문 종류(Type)가 있는 시스템(4M2T)과 10개의 머신-6개의 주문종류가 있는 시스템(10M6T)이다. 생성된 일정계획의 성능지표로는 100개의 주문을 처리하는데 발생하는 Setup Cost, 총 소요 생산시간(makespan)과 유휴가공시간(idle time)의 가중합이 활용되었다. 비교연구 결과 4M2T 시스템에서는 학습환경에 관계없이 학습된 시스템이 실험자보다 성능지표가 우수한 일정계획을 생성하였다. 10M6T 시스템의 경우 제안한 시스템이 단일 학습환경에서는 실험자보다 우수한 성능 지표의 일정계획을 생성하였으나 임의 학습환경에서는 실험자보다 부진한 성능지표를 보였다. 그러나 job Change 횟수 비교에서는 학습시스템이 4M2T, 10M6T 모두 사람보다 적은 결과를 나타내어 우수한 스케줄링 성능을 보였다.

• 한국항행학회논문지
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• 제16권1호
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• pp.76-81
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• 2012

다양한 모바일 기기의 보급 확산으로 u러닝 기반의 학습 관리 시스템의 연구가 활발히 진행 되고 있다. u-러닝 기반의 학습 관리 시스템은 콘텐츠 사용자의 접근 시간과 장소 그리고 다양한 접근 기기에 대한 제약이 없다는 점에서 매우 편리하다. 그러나 사용자에 대한 접근의 인증과 학습에 대한 집중 여부에 대한 판단이 매우 어렵다. 본 논문은 일반적인 사용자 이벤트 중심의 인터페이스가 아닌 음성과 사용자 안면 캡춰 인터페이스를 학습 관리 시스템에 적용 하였다. 사용자가 학습 관리 시스템에 접근 시 등록된 본인의 패스워드를 음성 입력하여 로그인 하고, 사용자가 콘텐츠를 통해 학습이 진행 되는 과정에서도 간단한 단어의 응답 발화를 통해 사용자의 학습 태도 및 학습 성과를 판단하게 한다. 제안된 학습 관리 시스템의 평가 결과 사용자의 학습 성취도와 집중도가 향상 되었으며 이에 따른 사용자의 비정상적인 학습태도에 대한 관리자의 모니터링을 가능 하게 했다.

• Educational Technology International
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• 제15권2호
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• pp.71-88
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• 2014

A variety of studies to predict students' performance have been conducted since educational data such as web-log files traced from Learning Management System (LMS) are increasingly used to analyze students' learning behaviors. However, it is still challenging to predict students' learning achievement in blended learning environment where online and offline learning are combined. In higher education, diverse cases of blended learning can be formed from simple use of LMS for administrative purposes to full usages of functions in LMS for online distance learning class. As a result, a generalized model to predict students' academic success does not fulfill diverse cases of blended learning. This study compares two blended learning classes with each prediction model. The first blended class which involves online discussion-based learning revealed a linear regression model, which explained 70% of the variance in total score through six variables including total log-in time, log-in frequencies, log-in regularities, visits on boards, visits on repositories, and the number of postings. However, the second case, a lecture-based class providing regular basis online lecture notes in Moodle show weaker results from the same linear regression model mainly due to non-linearity of variables. To investigate the non-linear relations between online activities and total score, RF (Random Forest) was utilized. The results indicate that there are different set of important variables for the two distinctive types of blended learning cases. Results suggest that the prediction models and data-mining technique should be based on the considerations of diverse pedagogical characteristics of blended learning classes.

• 한국경영과학회지
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• 제27권1호
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• pp.75-94
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• 2002

The development of Information technologies huts contributed on-line training as one of important education methods. On-line training In firms, which is similar to e-learning or virtual education, provides trainees with more education opportunities in diverse ways. It has developed a range of innovative services with an one-stop solution of education within the electronic sector. Also under the on-line training environment, trainees can undertake customized training packages at anytime and any places. Moreover, information technology allows both the trainers and other trainees to be decoupled in any of the elements of time, place, and space. Two research questions are investigated : what are the determinants affecting the on-line training effectiveness and how those variables effect the two aspects of training effectiveness: learning performance and transfer performance. Based on the previous literature conducted on the traditional training environment, the determinants of training effectiveness are derived. light hypotheses are developed based on literature reviews and tested by questionnaires survey data. The collected data have been analyzed by LISREL. It is found that the relationship between individual, organizational and on-line sloe design variables and training effectiveness (learning and transfer) are significant. The contribution and limitations of this research are also discussed tilth future studies.

• ETRI Journal
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• 제45권4호
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• pp.650-665
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• 2023

A novel smart metering technique capable of anomaly detection was proposed for real-time home power management system. Smart meter data generated in real-time were obtained from 900 households of single apartments. To detect outliers and missing values in smart meter data, a deep learning model, the autoencoder, consisting of a graph convolutional network and bidirectional long short-term memory network, was applied to the smart metering technique. Power management based on the smart metering technique was executed by multi-objective optimization in the presence of a battery storage system and an electric vehicle. The results of the power management employing the proposed smart metering technique indicate a reduction in electricity cost and amount of power supplied by the grid compared to the results of power management without anomaly detection.

• 품질경영학회지
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• 제50권4호
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• pp.701-716
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• 2022

Purpose: This study aims to predict the dry cargo transportation market economy. The subject of this study is the BDI (Baltic Dry Index) time-series, an index representing the dry cargo transport market. Methods: In order to increase the accuracy of the BDI time-series, we have pre-processed the original time-series via time-series decomposition and data augmentation techniques and have used them for ANN learning. The ANN algorithms used are Multi-Layer Perceptron (MLP), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) to compare and analyze the case of learning and predicting by applying time-series decomposition and data augmentation techniques. The forecast period aims to make short-term predictions at the time of t+1. The period to be studied is from '22. 01. 07 to '22. 08. 26. Results: Only for the case of the MAPE (Mean Absolute Percentage Error) indicator, all ANN models used in the research has resulted in higher accuracy (1.422% on average) in multivariate prediction. Although it is not a remarkable improvement in prediction accuracy compared to uni-variate prediction results, it can be said that the improvement in ANN prediction performance has been achieved by utilizing time-series decomposition and data augmentation techniques that were significant and targeted throughout this study. Conclusion: Nevertheless, due to the nature of ANN, additional performance improvements can be expected according to the adjustment of the hyper-parameter. Therefore, it is necessary to try various applications of multiple learning algorithms and ANN optimization techniques. Such an approach would help solve problems with a small number of available data, such as the rapidly changing business environment or the current shipping market.

• 한국환경복원기술학회지
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• 제25권6호
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• pp.25-33
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• 2022

In this study, a monitoring method using high-resolution images acquired by unmanned aerial vehicles and deep learning algorithms was proposed for the management of the Sinduri coastal sand dunes. Class classification was done using U-net, a semantic division method. The classification target classified 3 types of sand dune vegetation into 4 classes, and the model was trained and tested with a total of 320 training images and 48 test images. Ignored label was applied to improve the performance of the model, and then evaluated by applying two loss functions, CE Loss and BCE Loss. As a result of the evaluation, when CE Loss was applied, the value of mIoU for each class was the highest, but it can be judged that the performance of BCE Loss is better considering the time efficiency consumed in learning. It is meaningful as a pilot application of unmanned aerial vehicles and deep learning as a method to monitor and manage sand dune vegetation. The possibility of using the deep learning image analysis technology to monitor sand dune vegetation has been confirmed, and it is expected that the proposed method can be used not only in sand dune vegetation but also in various fields such as forests and grasslands.

• Educational Technology International
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• 제24권2호
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• pp.295-329
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• 2023

The recent development of metaverse-related technology has led to efforts to overcome the limitations of time and space in education by creating a virtual educational environment. To make use of this platform efficiently, applying learning analytics has been proposed as an optimal instructional and learning decision support approach to address these issues by identifying specific rules and patterns generated from learning data, and providing a systematic framework as a guideline to instructors. To achieve this, we employed an inductive, bottom-up approach for framework modeling. During the modeling process, based on the activity system model, we specifically derived the fundamental components of the learning analytics framework centered on learning activities and their contexts. We developed a prototype of the framework through deduplication, categorization, and proceduralization from the components, and refined the learning analytics framework into a 7-stage framework suitable for application in the metaverse through 3 steps of Delphi surveys. Lastly, through a framework model evaluation consisting of seven items, we validated the metaverse learning analytics framework, ensuring its validity.

• 한국IT서비스학회지
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• 제22권6호
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• pp.71-86
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• 2023

The rapid change in industry and the technological gap give rise to social demand for upskilling and reskilling and spread of alternative education. Against this backdrop, digital certification and career management tools can be used to manage various types of learning activities comprehensively. Digital badges provide various kinds of history information related to individual learning, and the reliability and transparency of the issued information can be strengthened by applying blockchain technology. There have been various discussions about digital badges for a long time, but due to the lack of standards to support the issuance and distribution of digital badges, they have been partially used in some areas. However, interest in digital badges is increasing due to the development of related technologies, establishment of standards, paradigm changes in higher education, and government policies related to nurturing digital talent. This paper deals with the use of digital badges for efficient and transparent learning management and career management in an online learning environment. The researcher analyzes the technical characteristics and use cases of digital badges, and proposes a plan for use in online higher education based on them.